Battery technologies along with monitoring and management systems for batteries are growing rapidly. Novel chemical compounds have been developed to build higher power and energy density batteries. From application perspectives, there is an ongoing need to develop intelligent algorithms to estimate the state of charge (SOC), state of health (SOH), and state of function (SOF) of batteries with high accuracy and robustness for real-time applications. Many battery models have been developed for different purposes, for example, an electrochemical model, an analytical model, an electric circuit model, etc.
The electrochemical model is known for its accuracy because it describes the chemical reaction inside the battery during charging/discharging. However, the model requires a full knowledge of the specific battery. Thus, it is difficult to use the electrochemical model in real-time applications due to the computation complexity of the model.
The analytical model, which is also known as the Kinetic battery model or the KiBam model, uses the kinetic process to model the chemical process of large lead-acid batteries. However, the analytical model adopts flat discharge profiles, which suits only such large lead-acid batteries, even though the analytical model captures the recovery effect and rate capacity effect.
The electric circuit model, also called Thevenin's circuit model, is commonly adopted to describe a battery characteristic, such as a transient response of a battery.
Algorithms for battery SOC, SOH, SOF estimation include: Coulomb counting as described in C. Y. Chun, J. Baek, G.-S. Seo, B. H. Cho, J. Kim, I. K. Chang, and S. Lee, “Current sensor-less state-of-charge estimation algorithm for lithium-ion batteries utilizing filtered terminal voltage,” J. Power Sources, vol. 273, no. 0, pp. 255-263, 2015, observer as described in M. a. Roscher, O. S. Bohlen, and D). U. Sauer, “Reliable state estimation of multicell Lithium-ion battery systems,” IEEE Trans. Energy Convers., vol. 26, no. 3, pp. 737-743, 2011, Kalman filter as described in S. Sepasi, R. Ghorbani, and B. Y. Liaw, “A novel on-board state-of charge estimation method for aged Li-ion batteries based on model adaptive extended Kalman filter,” J. Power Sources, vol. 245, pp. 337-344, 2014, Fuzzy logic as described in L. Kang, X. Zhao, and J. Ma, “A new neural network model for the state-of-charge estimation in the battery degradation process,” Appl. Energy, vol. 121, pp. 20-27, 2014. Most of these algorithms are model-based. Model-based approaches often face a serious problem: how to acquire accurate parameters to build a correct model. Accordingly, what is needed is a method to identify on-line battery measurements that may be used to determine accurate parameters of the model.
The foregoing “Background” description is for the purpose of generally presenting the context of the disclosure. Work of the inventors, to the extent it is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly or impliedly admitted as prior art against the present invention.